The grubbs_test function in the hana_ml.algorithms.pal.stats module performs Grubbs' test to detect outliers in a given univariate data set, assuming the data comes from a Gaussian distribution, and returns test results and statistics.
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Here is a Python code template based on the provided help documentation:

```python
# Import necessary libraries
from hana_ml.algorithms.pal.stats import grubbs_test
from hana_ml import DataFrame

# Assuming connection context `cc` is already defined and a connection to HANA is established

# Define the data
data = DataFrame(cc, 'SELECT * FROM <your_table>')  # replace <your_table> with your actual table name

# Define the parameters
key = '<key_column>'  # replace <key_column> with your actual key column name
col = '<col_column>'  # replace <col_column> with your actual column name
method = 'one_side_max'  # choose from 'two_sides', 'one_side_min', 'one_side_max', 'repeat_two_sides'
alpha = 0.2  # significance level

# Perform the Grubbs' test
res, stats = grubbs_test(data=data, key=key, col=col, method=method, alpha=alpha)

# Print the results
print("Test results:")
print(res.collect())
print("\nStatistics:")
print(stats.collect())
```

Please replace `<your_table>`, `<key_column>`, and `<col_column>` with your actual table name and column names. Also, make sure that a connection to HANA is established and the connection context `cc` is defined.